OpenRaiser/Intern-Atlas huggingface dataset:
On Fair Selection in the Presence of Implicit Variance
Venue: arXiv cs.CY 2020
Year: 2020

Quota-based fairness mechanisms like the so-called Rooney rule or four-fifths rule are used in selection problems such as hiring or college admission to reduce inequalities based on sensitive demographic attributes. These mechanisms are often viewed as introducing a trade-off between selection fairness and utility. In recent work, however, Kleinberg and Raghavan showed that, in the presence of implicit bias in estimating candidates' quality, the Rooney rule can increase the utility of the selection process. We argue that even in the absence of implicit bias, the estimates of candidates' quality from different groups may differ in another fundamental way, namely, in their variance. We term this phenomenon implicit variance and we ask: can fairness mechanisms be beneficial to the utility of a selection process in the presence of implicit variance (even in the absence of implicit bias)? To answer this question, we propose a simple model in which candidates have a true latent quality that is drawn from a group-independent normal distribution. To make the selection, a decision maker receives an unbiased estimate of the quality of each candidate, with normal noise, but whose variance depends on the candidate's group. We then compare the utility obtained by imposing a fairness mechanism that we term $γ$-rule (it includes demographic parity and the four-fifths rule as special cases), to that of a group-oblivious selection algorithm that picks the candidates with the highest estimated quality independently of their group. Our main result shows that the demographic parity mechanism always increases the selection utility, while any $γ$-rule weakly increases it. We extend our model to a two-stage selection process where the true quality is observed at the second stage. We discuss multiple extensions of our results, in particular to different distributions of the true latent quality.
Quantum Computer: Hello, Music!
Venue: arXiv cs.ET 2020
Year: 2020

Quantum computing is emerging as a promising technology, which is built on the principles of subatomic physics. By the time of writing, fully fledged practical quantum computers are not widely available. But research and development are advancing rapidly. Various software simulators are already available. And a few companies have already started to provide access to quantum hardware via the cloud. These initiatives have enabled experiments with quantum computing to tackle some realistic problems in science; e.g., in chemistry and cryptography. In spite of continuing progress in developing increasingly more sophisticated hardware and software, research in quantum computing has been focusing primarily on developing scientific applications. Up till now there has been virtually no research activity aimed at widening the range of applications of this technology beyond science and engineering. In particular applications for the entertainment industry and creative economies. This article introduces a new field of research, which is referred to as Quantum Computer Music. This research is aimed at the development of quantum computing tools and approaches to creating, performing, listening to and distributing music. The article begins with a brief historical background. Then, it introduces the notion of algorithmic music and presents two quantum computer music systems: a singing voice synthesiser and a musical sequencer based on quantum walk. A primer on quantum computing is also given. The chapter ends with a concluding discussion and advice for further work to develop this new exciting area of research.
AReLU: Attention-based Rectified Linear Unit
Venue: arXiv cs.LG 2020
Year: 2020

Element-wise activation functions play a critical role in deep neural networks via affecting the expressivity power and the learning dynamics. Learning-based activation functions have recently gained increasing attention and success. We propose a new perspective of learnable activation function through formulating them with element-wise attention mechanism. In each network layer, we devise an attention module which learns an element-wise, sign-based attention map for the pre-activation feature map. The attention map scales an element based on its sign. Adding the attention module with a rectified linear unit (ReLU) results in an amplification of positive elements and a suppression of negative ones, both with learned, data-adaptive parameters. We coin the resulting activation function Attention-based Rectified Linear Unit (AReLU). The attention module essentially learns an element-wise residue of the activated part of the input, as ReLU can be viewed as an identity transformation. This makes the network training more resistant to gradient vanishing. The learned attentive activation leads to well-focused activation of relevant regions of a feature map. Through extensive evaluations, we show that AReLU significantly boosts the performance of most mainstream network architectures with only two extra learnable parameters per layer introduced. Notably, AReLU facilitates fast network training under small learning rates, which makes it especially suited in the case of transfer learning and meta learning. Our source code has been released (see https://github.com/densechen/AReLU).
Multi-scale modelling of concrete structures affected by alkali-silica reaction: Coupling the mesoscopic damage evolution and the macroscopic concrete deterioration
Venue: arXiv cs.CE 2020
Year: 2020

A finite-element approach based on the first-order FE 2 homogenisation technique is formulated to analyse the alkali-silica reaction-induced damage in concrete structures, by linking the concrete degradation at the macro-scale to the reaction extent at the meso-scale. At the meso-scale level, concrete is considered as a heterogeneous material consisting of aggregates embedded in a mortar matrix. The mechanical effects of the Alkali-Silica Reaction (ASR) are modelled through the application of temperature-dependent eigenstrains in several localised spots inside the aggregates and the mechanical degradation of concrete is modelled using continuous damage model, which is capable of reproducing the complex ASR crack networks. Then, the effective stiffness tensor and the effective stress tensor for each macroscopic finite element are computed by homogenising the mechanical response of the corresponding representative volume element (RVE), thus avoiding the use of phenomenological constitutive laws at the macro-scale. Convergence between macro- and meso-scales is achieved via an iterative procedure. A 2D model of an ASR laboratory specimen is analysed as a proof of concept. The model is able to account for the loading applied at the macro-scale and the ASR-product expansion at the meso-scale. The results demonstrate that the macroscopic stress state influences the orientation of damage inside the underlying RVEs. The effective stiffness becomes anisotropic in cases where damage is aligned inside the RVE.
Effective Elastic Scaling of Deep Learning Workloads
Venue: arXiv cs.DC 2020
Year: 2020

The increased use of deep learning (DL) in academia, government and industry has, in turn, led to the popularity of on-premise and cloud-hosted deep learning platforms, whose goals are to enable organizations utilize expensive resources effectively, and to share said resources among multiple teams in a fair and effective manner. In this paper, we examine the elastic scaling of Deep Learning (DL) jobs over large-scale training platforms and propose a novel resource allocation strategy for DL training jobs, resulting in improved job run time performance as well as increased cluster utilization. We begin by analyzing DL workloads and exploit the fact that DL jobs can be run with a range of batch sizes without affecting their final accuracy. We formulate an optimization problem that explores a dynamic batch size allocation to individual DL jobs based on their scaling efficiency, when running on multiple nodes. We design a fast dynamic programming based optimizer to solve this problem in real-time to determine jobs that can be scaled up/down, and use this optimizer in an autoscaler to dynamically change the allocated resources and batch sizes of individual DL jobs. We demonstrate empirically that our elastic scaling algorithm can complete up to $\approx 2 \times$ as many jobs as compared to a strong baseline algorithm that also scales the number of GPUs but does not change the batch size. We also demonstrate that the average completion time with our algorithm is up to $\approx 10 \times$ faster than that of the baseline.
Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics
Venue: arXiv cs.CV 2020
Year: 2020

We propose an automatic method for pain intensity measurement from video. For each video, pain intensity was measured using the dynamics of facial movement using 66 facial points. Gram matrices formulation was used for facial points trajectory representations on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank. Curve fitting and temporal alignment were then used to smooth the extracted trajectories. A Support Vector Regression model was then trained to encode the extracted trajectories into ten pain intensity levels consistent with the Visual Analogue Scale for pain intensity measurement. The proposed approach was evaluated using the UNBC McMaster Shoulder Pain Archive and was compared to the state-of-the-art on the same data. Using both 5-fold cross-validation and leave-one-subject-out cross-validation, our results are competitive with respect to state-of-the-art methods.
K-Prototype Segmentation Analysis on Large-scale Ridesourcing Trip Data
Venue: arXiv cs.CY 2020
Year: 2020

Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they impact travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examines emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data are matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcings role in Chicagos mobility system. The goal is to investigate the systematic variations in patronage of ride-hailing. K-prototypes is utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output is a classification of the data into several clusters called prototypes. Six ridesourcing prototypes are identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses implications of the identified clusters related to affordability, equity and competition with transit.
Developing of a photonic hardware platform for brain-inspired computing based on $5\times5$ VCSEL arrays
Venue: arXiv cs.ET 2020
Year: 2020

Brain-inspired computing concepts like artificial neural networks have become promising alternatives to classical von Neumann computer architectures. Photonic neural networks target the realizations of neurons, network connections and potentially learning in photonic substrates. Here, we report the development of a nanophotonic hardware platform of fast and energy-efficient photonic neurons via arrays of high-quality vertical cavity surface emitting lasers (VCSELs). The developed $5\times5$ VCSEL arrays provide high optical injection locking efficiency through homogeneous fabrication combined with individual control over the laser wavelengths. Injection locking is crucial for the reliable processing of information in VCSEL-based photonic neurons, and we demonstrate the suitability of the VCSEL arrays by injection locking measurements and current-induced spectral fine-tuning. We find that our investigated array can readily be tuned to the required spectral homogeneity, and as such show that VCSEL arrays based on our technology can act as highly energy efficient and ultra-fast photonic neurons for next generation photonic neural networks. Combined with fully parallel photonic networks our substrates are promising for ultra-fast operation reaching 10s of GHz bandwidths, and we show that a nonlinear transformation based on our lasers will consume only about 100 fJ per VCSEL, which is highly competitive, compared to other platforms.
Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Existing face datasets often lack sufficient representation of occluding objects, which can hinder recognition, but also supply meaningful information to understand the visual context. In this work, we introduce Extended Labeled Faces in-the-Wild (ELFW), a dataset supplementing with additional face-related categories -- and also additional faces -- the originally released semantic labels in the vastly used Labeled Faces in-the-Wild (LFW) dataset. Additionally, two object-based data augmentation techniques are deployed to synthetically enrich under-represented categories which, in benchmarking experiments, reveal that not only segmenting the augmented categories improves, but also the remaining ones benefit.
Scalable Load Balancing in the Presence of Heterogeneous Servers
Venue: arXiv cs.PF 2020
Year: 2020

Heterogeneity is becoming increasingly ubiquitous in modern large-scale computer systems. Developing good load balancing policies for systems whose resources have varying speeds is crucial in achieving low response times. Indeed, how best to dispatch jobs to servers is a classical and well-studied problem in the queueing literature. Yet the bulk of existing work on large-scale systems assumes homogeneous servers; unfortunately, policies that perform well in the homogeneous setting can cause unacceptably poor performance---or even instability---in heterogeneous systems. We adapt the "power-of-d" versions of both the Join-the-Idle-Queue and Join-the-Shortest-Queue policies to design two corresponding families of heterogeneity-aware dispatching policies, each of which is parameterized by a pair of routing probabilities. Unlike their heterogeneity-unaware counterparts, our policies use server speed information both when choosing which servers to query and when probabilistically deciding where (among the queried servers) to dispatch jobs. Both of our policy families are analytically tractable: our mean response time and queue length distribution analyses are exact as the number of servers approaches infinity, under standard assumptions. Furthermore, our policy families achieve maximal stability and outperform well-known dispatching rules---including heterogeneity-aware policies such as Shortest-Expected-Delay---with respect to mean response time.
Mesh deformation techniques in fluid-structure interaction: robustness, accumulated distortion and computational efficiency
Venue: arXiv cs.CE 2020
Year: 2020

An important ingredient of any moving-mesh method for fluid-structure interaction (FSI) problems is the mesh deformation technique (MDT) used to adapt the computational mesh in the moving fluid domain. An ideal technique is computationally inexpensive, can handle large mesh deformations without inverting mesh elements and can sustain an FSI simulation for extensive periods ot time without irreversibly distorting the mesh. Here we compare several commonly used techniques based on the solution of elliptic partial differential equations, including harmonic extension, bi-harmonic extension and techniques based on the equations of linear elasticity. Moreover, we propose a novel technique which utilizes ideas from continuation methods to efficiently solve the equations of nonlinear elasticity and proves to be robust even when the mesh is subject to extreme deformations. In addition to that, we study how each technique performs when combined with the Jacobian-based local stiffening. We evaluate each technique on a popular two-dimensional FSI benchmark reproduced by using an isogeometric partitioned solver with strong coupling.
Ensuring Learning Guarantees on Concept Drift Detection with Statistical Learning Theory
Venue: arXiv cs.LG 2020
Year: 2020

Concept Drift (CD) detection intends to continuously identify changes in data stream behaviors, supporting researchers in the study and modeling of real-world phenomena. Motivated by the lack of learning guarantees in current CD algorithms, we decided to take advantage of the Statistical Learning Theory (SLT) to formalize the necessary requirements to ensure probabilistic learning bounds, so drifts would refer to actual changes in data rather than by chance. As discussed along this paper, a set of mathematical assumptions must be held in order to rely on SLT bounds, which are especially controversial in CD scenarios. Based on this issue, we propose a methodology to address those assumptions in CD scenarios and therefore ensure learning guarantees. Complementary, we assessed a set of relevant and known CD algorithms from the literature in light of our methodology. As main contribution, we expect this work to support researchers while designing and evaluating CD algorithms on different domains.
A Protocol to Convert Infrastructure Data from Computer-Aided Design (CAD) to Geographic Information Systems (GIS)
Venue: arXiv cs.CY 2020
Year: 2020

While many municipalities and organizations see value in converting infrastructure data from Computer-Aided Design (CAD) to Geographic Information System (GIS) format, the process can be complex, expensive, and time-consuming. Given that municipal employees often prefer to continue performing work in both CAD and GIS, depending on the type of work required, an improved conversion process would help municipalities more fully employ GIS-based analyses. Municipalities facing budget and capacity challenges would especially benefit from an improved conversion process. With advances in GIS functionality and the promise of smart and connected cities, more emphasis is placed on the quality of data, and in this case, the potential loss of data quality from CAD to GIS formats. The goals of this article are twofold. First, to understand the common practices municipalities use to convert infrastructure CAD data to GIS and the specific challenges they face. Second, based on knowledge of those practices and challenges, this article proposes a five-step process to reduce common conversion errors and reduce the time required to correct these errors. The process is illustrated through the conversion of CAD data from the University of Illinois at Chicago (UIC) campus. The findings were validated with qualitative, semi-structured interviews conducted with GIS Analysts and Managers working in eleven municipalities across the United States who directly manage at least one of the following infrastructures: water, sanitary sewer, or stormwater sewer systems. The interviews confirmed the challenges municipalities faced with the conversion and identified solutions interviewees undertook to enable data-informed decision-making.
The Max-Cut Decision Tree: Improving on the Accuracy and Running Time of Decision Trees
Venue: arXiv cs.LG 2020
Year: 2020

Decision trees are a widely used method for classification, both by themselves and as the building blocks of multiple different ensemble learning methods. The Max-Cut decision tree involves novel modifications to a standard, baseline model of classification decision tree construction, precisely CART Gini. One modification involves an alternative splitting metric, maximum cut, based on maximizing the distance between all pairs of observations belonging to separate classes and separate sides of the threshold value. The other modification is to select the decision feature from a linear combination of the input features constructed using Principal Component Analysis (PCA) locally at each node. Our experiments show that this node-based localized PCA with the novel splitting modification can dramatically improve classification, while also significantly decreasing computational time compared to the baseline decision tree. Moreover, our results are most significant when evaluated on data sets with higher dimensions, or more classes; which, for the example data set CIFAR-100, enable a 49% improvement in accuracy while reducing CPU time by 94%. These introduced modifications dramatically advance the capabilities of decision trees for difficult classification tasks.
Optimizing Affine Maximizer Auctions via Linear Programming: an Application to Revenue Maximizing Mechanism Design for Zero-Day Exploits Markets
Venue: arXiv cs.GT 2020
Year: 2020

Optimizing within the affine maximizer auctions (AMA) is an effective approach for revenue maximizing mechanism design. The AMA mechanisms are strategy-proof and individually rational (if the agents' valuations for the outcomes are nonnegative). Every AMA mechanism is characterized by a list of parameters. By focusing on the AMA mechanisms, we turn mechanism design into a value optimization problem, where we only need to adjust the parameters. We propose a linear programming based heuristic for optimizing within the AMA family. We apply our technique to revenue maximizing mechanism design for zero-day exploit markets. We show that due to the nature of the zero-day exploit markets, if there are only two agents (one offender and one defender), then our technique generally produces a near optimal mechanism: the mechanism's expected revenue is close to the optimal revenue achieved by the optimal strategy-proof and individually rational mechanism (not necessarily an AMA mechanism).
On Integrated Access and Backhaul Networks: Current Status and Potentials
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we introduce and study the potentials and challenges of integrated access and backhaul (IAB) as one of the promising techniques for evolving 5G networks. We study IAB networks from different perspectives. We summarize the recent Rel-16 as well as the upcoming Rel-17 3GPP discussions on IAB, and highlight the main IAB-specific agreements on different protocol layers. Also, concentrating on millimeter wave-based communications, we evaluate the performance of IAB networks in both dense and suburban areas. Using a finite stochastic geometry model, with random distributions of IAB nodes as well as user equipments (UEs) in a finite region, we study the service coverage rate defined as the probability of the event that the UEs' minimum rate requirements are satisfied. We present comparisons between IAB and hybrid IAB/fiber-backhauled networks where a part or all of the small base stations are fiber-connected. Finally, we study the robustness of IAB networks to weather and various deployment conditions and verify their effects, such as blockage, tree foliage, rain as well as antenna height/gain on the coverage rate of IAB setups, as the key differences between the fiber-connected and IAB networks. As we show, IAB is an attractive approach to enable the network densification required by 5G and beyond.